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So how are we going to beat this novel coronavirus?
By using our best tools:
our science and our technology.
In my lab, we're using the tools of artificial intelligence
and synthetic biology
to speed up the fight against this pandemic.
Our work was originally designed
to tackle the antibiotic resistance crisis.
Our project seeks to harness the power of machine learning
to replenish our antibiotic arsenal
and avoid a globally devastating postantibiotic era.
Importantly, the same technology can be used
to search for antiviral compounds
that could help us fight the current pandemic.
Machine learning is turning the traditional model of drug discovery
on its head.
With this approach,
instead of painstakingly testing thousands of existing molecules
one by one in a lab
for their effectiveness,
we can train a computer to explore the exponentially larger space
of essentially all possible molecules that could be synthesized,
and thus, instead of looking for a needle in a haystack,
we can use the giant magnet of computing power
to find many needles in multiple haystacks simultaneously.
We've already had some early success.
Recently, we used machine learning to discover new antibiotics
that can help us fight off the bacterial infections
that can occur alongside SARS-CoV-2 infections.
Two months ago, TED's Audacious Project approved funding for us
to massively scale up our work
with the goal of discovering seven new classes of antibiotics
against seven of the world's deadly bacterial pathogens
over the next seven years.
For context:
the number of new class of antibiotics
that have been discovered over the last three decades is zero.
While the quest for new antibiotics is for our medium-term future,
the novel coronavirus poses an immediate deadly threat,
and I'm excited to share that we think we can use the same technology
to search for therapeutics to fight this virus.
So how are we going to do it?
Well, we're creating a compound training library
and with collaborators applying these molecules to SARS-CoV-2-infected cells
to see which of them exhibit effective activity.
These data will be use to train a machine learning model
that will be applied to an in silico library of over a billion molecules
to search for potential novel antiviral compounds.
We will synthesize and test the top predictions
and advance the most promising candidates into the clinic.
Sound too good to be true?
Well, it shouldn't.
The Antibiotics AI Project is founded on our proof of concept research
that led to the discovery of a novel broad-spectrum antibiotic
called halicin.
Halicin has potent antibacterial activity
against almost all antibiotic-resistant bacterial pathogens,
including untreatable panresistant infections.
Importantly, in contrast to current antibiotics,
the frequency at which bacteria develop resistance against halicin
is remarkably low.
We tested the ability of bacteria to evolve resistance against halicin
as well as Cipro in the lab.
In the case of Cipro,
after just one day, we saw resistance.
In the case of halicin,
after one day, we didn't see any resistance.
Amazingly, after even 30 days,
we didn't see any resistance against halicin.
In this pilot project, we first tested roughly 2,500 compounds against E. coli.
This training set included known antibiotics,
such as Cipro and penicillin,
as well as many drugs that are not antibiotics.
These data we used to train a model
to learn molecular features associated with antibacterial activity.
We then applied this model to a drug-repurposing library
consisting of several thousand molecules
and asked the model to identify molecules
that are predicted to have antibacterial properties
but don't look like existing antibiotics.
Interestingly, only one molecule in that library fit these criteria,
and that molecule turned out to be halicin.
Given that halicin does not look like any existing antibiotic,
it would have been impossible for a human, including an antibiotic expert,
to identify halicin in this manner.
Imagine now what we could do with this technology
against SARS-CoV-2.
And that's not all.
We're also using the tools of synthetic biology,
tinkering with DNA and other cellular machinery,
to serve human purposes like combating COVID-19,
and of note, we are working to develop a protective mask
that can also serve as a rapid diagnostic test.
So how does that work?
Well, we recently showed
that you can take the cellular machinery out of a living cell
and freeze-dry it along with RNA sensors onto paper
in order to create low-cost diagnostics for Ebola and Zika.
The sensors are activated when they're rehydrated by a patient sample
that could consist of blood or saliva, for example.
It turns out, this technology is not limited to paper
and can be applied to other materials, including cloth.
For the COVID-19 pandemic,
we're designing RNA sensors to detect the virus
and freeze-drying these along with the needed cellular machinery
into the fabric of a face mask,
where the simple act of breathing,
along with the water vapor that comes with it,
can activate the test.
Thus, if a patient is infected with SARS-CoV-2,
the mask will produce a fluorescent signal
that could be detected by a simple, inexpensive handheld device.
In one or two hours, a patient could thus be diagnosed
safely, remotely and accurately.
We're also using synthetic biology
to design a candidate vaccine for COVID-19.
We are repurposing the BCG vaccine,
which had been used against TB for almost a century.
It's a live attenuated vaccine,
and we're engineering it to express SARS-CoV-2 antigens,
which should trigger the production of protective antibodies
by the immune system.
Importantly, BCG is massively scalable
and has a safety profile that's among the best of any reported vaccine.
With the tools of synthetic biology and artificial intelligence,
we can win the fight against this novel coronavirus.
This work is in its very early stages, but the promise is real.
Science and technology can give us an important advantage
in the battle of human wits versus the genes of superbugs,
a battle we can win.
Thank you.
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How we're using AI to discover new antibiotics | Jim Collins

25 分類 收藏
林宜悉 發佈於 2020 年 7 月 3 日
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